DEEP LEARNING OF SEMI-COMPETING RISK DATA VIA A NEW NEURAL EXPECTATION-MAXIMIZATION ALGORITHM

Prognostication for lung cancer, a leading cause of mortality, remains a complex task, as it needs to quantify the associations of risk factors and health events spanning a patient's entire life. One challenge is that an individual's disease course involves non-terminal (e.g., disease progression) and terminal (e.g., death) events, which form semi-competing relationships. Our motivation comes from the Boston Lung Cancer Study, a large lung cancer survival cohort, which investigates how risk factors influence a patient's disease trajectory. Following developments in the prediction of time-to-event outcomes with neural networks, deep learning has become a focal area for the development of risk prediction methods in survival analysis. However, limited work has been done to predict multi-state or semi-competing risk outcomes, where a patient may experience adverse events such as disease progression prior to death. We propose a neural expectation-maximization algorithm for semi-competing risks to bridge the gap between classical semi-competing survival models and deep learning. Our algorithm enables estimation of the nonparametric baseline hazards of each state transition, risk functions of predictors, and the degree of dependence among different transitions, via a multitask deep neural network with transition-specific sub-architectures. We apply our method to the Boston Lung Cancer Study and investigate the impact of clinical and genetic predictors on disease progression and mortality.